Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/262349 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
ECONtribute Discussion Paper No. 187
Verlag: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
Zusammenfassung: 
A central question in designing optimal policies concerns the assignment of individuals with different observable characteristics to different treatments. We study this question in the context of increasing workers' performance by using targeted incentives based on measurable worker characteristics. To do so, we ran two large-scale experiments. The key results are that (i) performance can be predicted by accurately measured personality traits, (ii) a machine learning algorithm can detect such heterogeneity in worker responses to different schemes, and (iii) a targeted assignment of schemes to individual workers increases performance in a second experiment significantly above the level achieved by the single best scheme.
Schlagwörter: 
Randomized Controlled Trial
Incentives
Heterogeneity
Treatment Effects
Selection
Algorithm
Dokumentart: 
Working Paper

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